Sequential Machine Learning demonstrates the concepts of machine learning with big data modeling examples for email spam detection, tweet topic modeling, and algorithmic trading, while shying away from too much theory. This book will guide hackers and devops people to a practical understanding of the mathematics and jargon of sequential machine learning.

You'll focus on Vowpal Wabbit, a powerful open source project for sequential machine learning with a serious learning curve. The payoff for this learning curve is important, since sequential models can be trained on the largest data sets around, such as the click stream of web banner advertisements, stock market ticks, or the Twitter firehose.

Ben has been a professional software developer for ten years, and has been hacking code for much longer. His past and current clients include “bulge bracket” investment banks, hedge funds and energy trading houses. He built a taxonomy browser for Encyclopaedia Britannica in 2004, and worked for ThoughtWorks in 2000. Ben teaches and advises on software engineering, machine learning, personal finance, financial analysis, and the culture of quants. He has an MSc in Finance from London Business School and a BEng in Computer Science from Northwestern University.